Generalizing Fair Top-$k$ Selection: An Integrative Approach
Researchers have developed a new approach to fair top-k selection, which aims to ensure proportional representation for minority groups among selected candidates. This generalized method considers multiple protected groups and seeks to minimize disparity from a reference scoring function. While the problem can become computationally intractable with multiple groups, the researchers identified a gap in the hardness barrier that allows for efficient solutions when the number of groups is small and k is also small. The study also introduces a new disparity measure called utility loss, which may lead to more stable scoring functions, and demonstrates strong empirical performance on real-world datasets. AI